{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-08T07:36:12.788492Z",
     "start_time": "2025-01-08T07:36:10.980747Z"
    }
   },
   "source": [
    "#实战美国911数据\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# 把时间字符串转为时间类型设置为索引\n",
    "df = pd.read_csv(\"./911.csv\")\n",
    "print(df.info())#查看数据框的基本信息\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "\n",
    "# 添加列，表示分类\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist() #二维列表。将title列中的字符串按\": \"拆分\n",
    "cate_list = [i[0] for i in temp_list] #i[0]就是EMS  Fire  Traffic。提取拆分后的第一个元素，即事件的分类\n",
    " \n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    "\n",
    " # 将timeStamp列设置为数据框的索引。inplace=True表示在原数据框上修改\n",
    "df.set_index(\"timeStamp\", inplace=True) \n",
    "\n",
    "df.head(10)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 249737 entries, 0 to 249736\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        249737 non-null  float64\n",
      " 1   lng        249737 non-null  float64\n",
      " 2   desc       249737 non-null  object \n",
      " 3   zip        219391 non-null  float64\n",
      " 4   title      249737 non-null  object \n",
      " 5   timeStamp  249737 non-null  object \n",
      " 6   twp        249644 non-null  object \n",
      " 7   addr       249737 non-null  object \n",
      " 8   e          249737 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 17.1+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                           lat        lng  \\\n",
       "timeStamp                                   \n",
       "2015-12-10 17:10:52  40.297876 -75.581294   \n",
       "2015-12-10 17:29:21  40.258061 -75.264680   \n",
       "2015-12-10 14:39:21  40.121182 -75.351975   \n",
       "2015-12-10 16:47:36  40.116153 -75.343513   \n",
       "2015-12-10 16:56:52  40.251492 -75.603350   \n",
       "2015-12-10 15:39:04  40.253473 -75.283245   \n",
       "2015-12-10 16:46:48  40.182111 -75.127795   \n",
       "2015-12-10 16:17:05  40.217286 -75.405182   \n",
       "2015-12-10 16:51:42  40.289027 -75.399590   \n",
       "2015-12-10 17:35:41  40.102398 -75.291458   \n",
       "\n",
       "                                                                  desc  \\\n",
       "timeStamp                                                                \n",
       "2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "2015-12-10 15:39:04  CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...   \n",
       "2015-12-10 16:46:48  LAUREL AVE & OAKDALE AVE;  HORSHAM; Station 35...   \n",
       "2015-12-10 16:17:05  COLLEGEVILLE RD & LYWISKI RD;  SKIPPACK; Stati...   \n",
       "2015-12-10 16:51:42  MAIN ST & OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...   \n",
       "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...   \n",
       "\n",
       "                         zip                        title                twp  \\\n",
       "timeStamp                                                                      \n",
       "2015-12-10 17:10:52  19525.0       EMS: BACK PAINS/INJURY        NEW HANOVER   \n",
       "2015-12-10 17:29:21  19446.0      EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n",
       "2015-12-10 14:39:21  19401.0          Fire: GAS-ODOR/LEAK         NORRISTOWN   \n",
       "2015-12-10 16:47:36  19401.0       EMS: CARDIAC EMERGENCY         NORRISTOWN   \n",
       "2015-12-10 16:56:52      NaN               EMS: DIZZINESS   LOWER POTTSGROVE   \n",
       "2015-12-10 15:39:04  19446.0             EMS: HEAD INJURY           LANSDALE   \n",
       "2015-12-10 16:46:48  19044.0         EMS: NAUSEA/VOMITING            HORSHAM   \n",
       "2015-12-10 16:17:05  19426.0   EMS: RESPIRATORY EMERGENCY           SKIPPACK   \n",
       "2015-12-10 16:51:42  19438.0        EMS: SYNCOPAL EPISODE      LOWER SALFORD   \n",
       "2015-12-10 17:35:41  19462.0  Traffic: VEHICLE ACCIDENT -           PLYMOUTH   \n",
       "\n",
       "                                                         addr  e     cate  \n",
       "timeStamp                                                                  \n",
       "2015-12-10 17:10:52                    REINDEER CT & DEAD END  1      EMS  \n",
       "2015-12-10 17:29:21                BRIAR PATH & WHITEMARSH LN  1      EMS  \n",
       "2015-12-10 14:39:21                                  HAWS AVE  1     Fire  \n",
       "2015-12-10 16:47:36                        AIRY ST & SWEDE ST  1      EMS  \n",
       "2015-12-10 16:56:52                  CHERRYWOOD CT & DEAD END  1      EMS  \n",
       "2015-12-10 15:39:04                     CANNON AVE & W 9TH ST  1      EMS  \n",
       "2015-12-10 16:46:48                  LAUREL AVE & OAKDALE AVE  1      EMS  \n",
       "2015-12-10 16:17:05              COLLEGEVILLE RD & LYWISKI RD  1      EMS  \n",
       "2015-12-10 16:51:42             MAIN ST & OLD SUMNEYTOWN PIKE  1      EMS  \n",
       "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1  Traffic  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>cate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeStamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:10:52</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:29:21</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 14:39:21</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:47:36</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:56:52</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 15:39:04</th>\n",
       "      <td>40.253473</td>\n",
       "      <td>-75.283245</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST;  LANSDALE; Station 345;...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: HEAD INJURY</td>\n",
       "      <td>LANSDALE</td>\n",
       "      <td>CANNON AVE &amp; W 9TH ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:46:48</th>\n",
       "      <td>40.182111</td>\n",
       "      <td>-75.127795</td>\n",
       "      <td>LAUREL AVE &amp; OAKDALE AVE;  HORSHAM; Station 35...</td>\n",
       "      <td>19044.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>LAUREL AVE &amp; OAKDALE AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:17:05</th>\n",
       "      <td>40.217286</td>\n",
       "      <td>-75.405182</td>\n",
       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD;  SKIPPACK; Stati...</td>\n",
       "      <td>19426.0</td>\n",
       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
       "      <td>SKIPPACK</td>\n",
       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:51:42</th>\n",
       "      <td>40.289027</td>\n",
       "      <td>-75.399590</td>\n",
       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>EMS: SYNCOPAL EPISODE</td>\n",
       "      <td>LOWER SALFORD</td>\n",
       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:35:41</th>\n",
       "      <td>40.102398</td>\n",
       "      <td>-75.291458</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD; PLYM...</td>\n",
       "      <td>19462.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>PLYMOUTH</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-08T07:31:00.500391Z",
     "start_time": "2025-01-08T07:30:59.505019Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "# 分组,一图多线\n",
    "#resample降采样，按月统计,索引必须是时间类型，类似groupby\n",
    "for group_name, group_data in df.groupby(by=\"cate\"):\n",
    "    # 对不同的分类都进行绘图\n",
    "    # MS表示每月的开始日（Month Start）。\n",
    "    # [\"title\"]表示对title列计数，即统计每月事件发生的次数。\n",
    "    count_by_month = group_data.resample(\"MS\").count()[\"title\"]  #降采样\n",
    "    print(count_by_month)\n",
    "    # 画图\n",
    "    _x = count_by_month.index\n",
    "    print(_x)\n",
    "    _y = count_by_month.values #values是对应事故发生次数\n",
    "\n",
    "    _x = [i.strftime(\"%Y%m%d\") for i in _x]  #变年月日格式\n",
    "\n",
    "    plt.plot(_x, _y, label=group_name)\n",
    "\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ],
   "id": "b2f646c75f54c176",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeStamp\n",
      "2015-12-01    3898\n",
      "2016-01-01    6063\n",
      "2016-02-01    5413\n",
      "2016-03-01    5832\n",
      "2016-04-01    5684\n",
      "2016-05-01    5730\n",
      "2016-06-01    5720\n",
      "2016-07-01    6029\n",
      "2016-08-01    6005\n",
      "2016-09-01    5750\n",
      "2016-10-01    6039\n",
      "2016-11-01    5838\n",
      "2016-12-01    6024\n",
      "2017-01-01    6082\n",
      "2017-02-01    5417\n",
      "2017-03-01    5913\n",
      "2017-04-01    5780\n",
      "2017-05-01    5948\n",
      "2017-06-01    6030\n",
      "2017-07-01    5974\n",
      "2017-08-01    5882\n",
      "2017-09-01    3789\n",
      "Freq: MS, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-01', '2016-01-01', '2016-02-01', '2016-03-01',\n",
      "               '2016-04-01', '2016-05-01', '2016-06-01', '2016-07-01',\n",
      "               '2016-08-01', '2016-09-01', '2016-10-01', '2016-11-01',\n",
      "               '2016-12-01', '2017-01-01', '2017-02-01', '2017-03-01',\n",
      "               '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',\n",
      "               '2017-08-01', '2017-09-01'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='MS')\n",
      "timeStamp\n",
      "2015-12-01    1095\n",
      "2016-01-01    1904\n",
      "2016-02-01    1868\n",
      "2016-03-01    1589\n",
      "2016-04-01    1717\n",
      "2016-05-01    1573\n",
      "2016-06-01    1787\n",
      "2016-07-01    1898\n",
      "2016-08-01    1907\n",
      "2016-09-01    1793\n",
      "2016-10-01    1930\n",
      "2016-11-01    1765\n",
      "2016-12-01    1846\n",
      "2017-01-01    1658\n",
      "2017-02-01    1462\n",
      "2017-03-01    1634\n",
      "2017-04-01    1614\n",
      "2017-05-01    1670\n",
      "2017-06-01    1986\n",
      "2017-07-01    1754\n",
      "2017-08-01    1862\n",
      "2017-09-01    1120\n",
      "Freq: MS, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-01', '2016-01-01', '2016-02-01', '2016-03-01',\n",
      "               '2016-04-01', '2016-05-01', '2016-06-01', '2016-07-01',\n",
      "               '2016-08-01', '2016-09-01', '2016-10-01', '2016-11-01',\n",
      "               '2016-12-01', '2017-01-01', '2017-02-01', '2017-03-01',\n",
      "               '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',\n",
      "               '2017-08-01', '2017-09-01'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='MS')\n",
      "timeStamp\n",
      "2015-12-01    2923\n",
      "2016-01-01    5129\n",
      "2016-02-01    4115\n",
      "2016-03-01    3638\n",
      "2016-04-01    3886\n",
      "2016-05-01    4071\n",
      "2016-06-01    4225\n",
      "2016-07-01    4161\n",
      "2016-08-01    3992\n",
      "2016-09-01    4126\n",
      "2016-10-01    4533\n",
      "2016-11-01    4488\n",
      "2016-12-01    4292\n",
      "2017-01-01    3865\n",
      "2017-02-01    3388\n",
      "2017-03-01    4137\n",
      "2017-04-01    3662\n",
      "2017-05-01    4101\n",
      "2017-06-01    4317\n",
      "2017-07-01    4040\n",
      "2017-08-01    4009\n",
      "2017-09-01    2367\n",
      "Freq: MS, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-01', '2016-01-01', '2016-02-01', '2016-03-01',\n",
      "               '2016-04-01', '2016-05-01', '2016-06-01', '2016-07-01',\n",
      "               '2016-08-01', '2016-09-01', '2016-10-01', '2016-11-01',\n",
      "               '2016-12-01', '2017-01-01', '2017-02-01', '2017-03-01',\n",
      "               '2017-04-01', '2017-05-01', '2017-06-01', '2017-07-01',\n",
      "               '2017-08-01', '2017-09-01'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='MS')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "###### 降采样是将数据的时间粒度降低（如按月、按年），减少数据量，常用于统计或趋势分析。\n",
    "升采样则是将数据的时间粒度提高（如按小时、按分钟），通常需要插值来填补新生成的时间点。"
   ],
   "id": "2bd294a3a16b74ad"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 分组练习",
   "id": "3eb15444e61c5c80"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-08T07:39:33.475895Z",
     "start_time": "2025-01-08T07:39:33.456721Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "#分组后给名称加前缀\n",
    "dict_obj = {'key1' : ['a', 'b', 'a', 'b',\n",
    "                      'a', 'b', 'a', 'a'],\n",
    "            'key2' : ['one', 'one', 'two', 'three',\n",
    "                      'two', 'two', 'one', 'three'],\n",
    "            'data1': np.random.randint(1, 10, 8),\n",
    "            'data2': np.random.randint(1, 10, 8)}\n",
    "df_obj = pd.DataFrame(dict_obj)\n",
    "print(df_obj)\n",
    "print('-'*50)\n"
   ],
   "id": "8eab4fd202937a56",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1   key2  data1  data2\n",
      "0    a    one      4      5\n",
      "1    b    one      6      6\n",
      "2    a    two      5      4\n",
      "3    b  three      2      8\n",
      "4    a    two      3      9\n",
      "5    b    two      9      3\n",
      "6    a    one      7      6\n",
      "7    a  three      9      5\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-08T07:40:23.914442Z",
     "start_time": "2025-01-08T07:40:23.900405Z"
    }
   },
   "cell_type": "code",
   "source": "df_obj.info()",
   "id": "b830f20909af6602",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   key1    8 non-null      object\n",
      " 1   key2    8 non-null      object\n",
      " 2   data1   8 non-null      int32 \n",
      " 3   data2   8 non-null      int32 \n",
      "dtypes: int32(2), object(2)\n",
      "memory usage: 324.0+ bytes\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-08T07:40:34.849209Z",
     "start_time": "2025-01-08T07:40:34.834050Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 按key1分组后，计算data1，data2的统计信息并附加到原始表格中，并添加表头前缀\n",
    "k1_sum = df_obj.groupby('key1').mean(numeric_only=True).add_prefix('mean_')\n",
    "#先按照k1分组得到一个数据。然后对这个数据进行mean操作，且只对数据型数据操作。然后把结果作为一个新的列写入到原来的表格。\n",
    "print(k1_sum)"
   ],
   "id": "47b3b4460ba5ca86",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      mean_data1  mean_data2\n",
      "key1                        \n",
      "a       5.600000    5.800000\n",
      "b       5.666667    5.666667\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-08T07:50:45.037567Z",
     "start_time": "2025-01-08T07:50:45.018007Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用transform，分组后计算结果和原本的df保持一致\n",
    "k1_sum_tf = df_obj.loc[:,['key1','data1', 'data2']].groupby('key1').transform('mean').add_prefix('mean_')\n",
    "#选择k1,d1,d2三列。transform('mean') 会将每个组的平均值分配回每一行，并保持每一行的原始索引。\n",
    "k1_sum_tf\n",
    "# df_obj[k1_sum_tf.columns] = k1_sum_tf\n",
    "# print(df_obj)"
   ],
   "id": "cb438e156fa840a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   mean_data1  mean_data2\n",
       "0    5.600000    5.800000\n",
       "1    5.666667    5.666667\n",
       "2    5.600000    5.800000\n",
       "3    5.666667    5.666667\n",
       "4    5.600000    5.800000\n",
       "5    5.666667    5.666667\n",
       "6    5.600000    5.800000\n",
       "7    5.600000    5.800000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_data1</th>\n",
       "      <th>mean_data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### 总结：\n",
    " 这里的 transform 会将每个组的平均值“广播”回每个组内的所有行。与 groupby().mean() 的操作不同，transform不会聚合成较少的行数，而是保留原始行数，给每一行的相应列赋予该组的平均值。"
   ],
   "id": "b16576cad59ce7c1"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "d05b089084d98471"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
